{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "#高斯\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "#神经网络\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris() #type:sklearn.datasets.base.Bunch\n",
    "\n",
    "X = iris.data[:, 0:2] #type: numpy.ndarray\n",
    "Y = iris.target;\n",
    "\n",
    "#X.shape  (150,2)\n",
    "\n",
    "n_features = X.shape[1] #获取特征数量\n",
    "Y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#创建不同的分类器\n",
    "\n",
    "C = 1.0\n",
    "kernel = 1.0 * RBF([1.0, 1.0])  # for GPC\n",
    "#C 正则化系数λ的倒数，float类型，默认为1.0。必须是正浮点型数。像SVM一样，越小的数值表示越强的正则化\n",
    "#penalty 惩罚项\n",
    "#probality :是否采用概率估计？.默认为False\n",
    "classifiers = {'L1 logistic': LogisticRegression(C=C, penalty='l1'),\n",
    "               'L2 logistic': LogisticRegression(C=C, penalty=\"l2\"),\n",
    "               'Linear SVC' : SVC(C=C, kernel=\"linear\", probability=True, random_state=0),\n",
    "               \"L2 logistic (multinomial) \" :LogisticRegression(\n",
    "                C=C, solver='lbfgs', multi_class='multinomial'),\n",
    "               \"GPC\":GaussianProcessClassifier(kernel)\n",
    "              }\n",
    "n_classifiers = len(classifiers)\n",
    "plt.figure(figsize=(3*2, n_classifiers*2))\n",
    "plt.subplots_adjust(bottom=.2, top=.96)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.          1.        ]\n",
      " [ 3.06060606  1.        ]\n",
      " [ 3.12121212  1.        ]\n",
      " ..., \n",
      " [ 8.87878788  5.        ]\n",
      " [ 8.93939394  5.        ]\n",
      " [ 9.          5.        ]]\n"
     ]
    }
   ],
   "source": [
    "xx = np.linspace(3,9,100) # 取3-9之前100个数\n",
    "yy = np.linspace(1,5,100).T\n",
    "xx, yy = np.meshgrid(xx, yy)  #numpy.meshgrid()——生成网格点坐标矩阵\n",
    "Xfull = np.c_[xx.ravel(), yy.ravel()]\n",
    "print(Xfull)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classif_rate for L1 logistic : 0.793333\n",
      "classif_rate for L2 logistic : 0.766667\n",
      "classif_rate for Linear SVC : 0.820000\n",
      "classif_rate for L2 logistic (multinomial)  : 0.820000\n",
      "classif_rate for GPC : 0.826667\n"
     ]
    },
    {
     "data": {
      "image/png": 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slK2PeDuOdZjAcsqWWc4eO8DZpX2MexVODmaZMDXy4ojuIx+q8Z5/XubB+yOK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z7IQgoBFwKtKLTCz5aO7zm0fUNWLq8+uZO4OXHYRfEGAwMaLVwEtOaCjnkWXyNUUlcBRXzlZFcG\nCh/BW6iOIv6PuPL/GOMaQBkXUJ3v0222dfr/M10ULW6ULG1Z2tcBkzvWb9PDMA6xIjWtyQBt16ZX\nSOsfkcK1bOtdU+hxE/hAeXHB/mp8bV7BFdgwOJ4hIKIYUcTzkOzTXTpHqPqL17olhYytZdsaW8ZI\nxibnfGZ7yU/LBrkY+LKq3i/dkoKLgy4ZZsVnfhdjLGFoMUaphAl9lZgFUZNqmLC4OsFoZZzllTEW\nBHWOCHezKBhnSBpEYonEunJQaogx7LEVtqcL2BiPMJb282R9MWvHF7Fp9xC79/RhGwEyHhJOCuGk\nSwoPGhA0wcTqHtLEP6wW1zw76xfb9lD7ffN12YOdzzb33PKXPUzbcw/RwkUc9dvvdzXr/DyaxFU5\nDuuKJBBNuVTNaDzBNC3BeAOZbCD1puuNE8eu9FYGI66XcxRBGKDVCtpXIe2LsNXAF2XwD1Phrsuv\nlXtbenp8P4+044mW9qZTWSMqG0r+MGYP6e3/8sHZAs+fs+iTAV4YXQRqey+5V2xJC4WmY6H7P4qg\n5pqPaRSQLqgSD0bECwyNQYOtQHNQSBZA0q/YimL7UqQvJawkhKGlGiXUKjG1MCE0lmqQUDGJE678\nW9mqkNgAi9BMfZZKGmBVXAP7NOCB3/unnuahF4b4MxG5ATgG+KiIDNJDMyv3sle/WAKjRMZSDRNq\nQUxfELMgaLAwmGTANBg2kwybOgOSEIkLVU8Bg6WCxYowITH9psGt/7GVb/75zezZPEk0OsTCX3s1\n/eee65+SQiOczpBcvy5jcl3p7rK+yAwPd2Qvi/zFkrYWk7iXj2laTCPFNFOkHruuifUG2BRin7ue\nwRjXewN/XcIAEl9INBEIDaROmpCi/NeNGSa2tc4/yK7dqAImv9td50B/vEqnIlBiX6KL7KRe4nc5\n5l5SNIp66VCMEgRKEFiiMKUapFQDxxBrQUzFpJ4hWqwarApGFOvfmM0UrBFEBZu6qji9qnO9MMS3\nA88HHlfVSRFZDLxt7nnQ/FOAwFgCYwnFqcYVk9AfNFwxUNOgJjE1SakKVMSVinI1cJVYXRmfiqT8\n9D+28v9/8gli358j3rqb7X//TWxsGDzjvGkq3HQJsMeZydApbMxhYjgsUJzTjBmluPaSqWsiZGKL\nxCkSJ5D4JU2nV0FXdQ9N6ipZS2q9lK65FKiIK8Dcab7w+4i6pkX5utTmLzZBXbl60fw1LlbzEvbZ\nbymZ4rOEbs+LSBtTdMwQpyobRbwQZURzvlEJUkJJqRjHHA3qmaB1hRvSECuC1RRrhNRXuMnU7V4x\nWxziyar6EI4ZAhw7h6bcMYC3FxZ+VGRSKkGaS4eDps6gmWLI1Bk0TfpFGRCDESFASJ31kAAlxnLT\nN3fydx/Z2laCHkCbMWPfuIHB018wTSp09qt2Vbkbk8wePk+6/yxMpJbMEFoSYnFOTYpjggmYppMQ\npZkgzQSasSvtVmSItjiv6hlhgIorM5+Xncedh4zRFbu95QyzZaeU1DrbpbVtOox4yUEla1yvLXuz\nKL3LDyXmxBzPSG72EFCT2Y0zW6IiXjIMA0ulIB1mkmFfEFM1CUZaFzjRAIPmZb6sGhKxGIRUDIHo\nzMblDswmIb4fV+brc91+F/CK2QbODKKBKIFxn6Gx9Hl1ud806TcNhkydftOgX1JqvpiowRCIgKZ8\n85op/ujK3azf4PqszvQWT7fvatkECw+qFJmhbbcHtjHD4i8rfGZjTtt+uKIwZ7kDK/VznUuHqWOI\njdjZDDuK/2qBIQqBe4i0UBh4HrSI9TZDrzKj/jOzIxrIuumJOPVYAvKWH9OkxBL7Bnbm2cykQ8cU\nHTMkUAgUCS2BZ4aVMKEaJPSHTVcL0yQ5Qwz8Gy/FkNgUgxJmDDFwrUgTawjU5qp0L5itL/Nv+8+X\n9zxaAVLwEmVSYih+MSmRSaiZmEgSapI6u6EYIml1Uf361ZO878NjTGVtKWe5Y4PFw23MMH9Yc8mQ\n1vYOpud/8PT1he2z2R0PN+TMMJfOsheQIolnSIlXjb1U2MYMM8Ynxv3f2SA+81TCjIFhuS04+z91\nTDBzqkg2ps08mIIY313Pt6hwTLCUDg8EVDqWNvuhJQzSgs/BMcJQUvpNk8ikROLUxFQNjYyN2YhE\nLE3RXKXOll5rZvfSU+UNXVaPAfep6pbuxxQkROOZoXGMsGoSqiah3zQZMA0GvO2wJr6Ssi8oalGu\nuLLADOdA7fSTffcuwSS4zl2ZGpe2HtrpEmFBTS5sa/dCazuTPEwZo3TaZguOFZOo75ZWsB02Y+dZ\nTgqOlKwAsPgCo1kbcfEVq42rVagm8yyLty+1vMwy7UXlVWbrmWKceKnTq+OBe9gIJHe4SOrsiNkL\n8jC9pDMje1ntDbLuezOOTZt0qJl0GFlCLxnW2qTDBgNBI+cbNRPnKrNVQyRRzvgsQqyGxBiwkIgl\nmUcl9F6dKi/Glf0CeBnwM+AYEfm0qv7zjL/b37nGc+zQWIy42MPIF26MxBKhRBLkRUXBVVd+akPv\nhR3r9z2EXOw8nZnHs+gBbUk1HYb5DAWGV2SA05hhiXa1OVNbi7a8xPXkVWudZJipTx3V0FtSoItV\nk8CX9Q98EVcfs4Znjq3TO5tQZsvM4xszydC3uwTy48QYNPUG9lSRMB+sRU8pLfaOvS2mm0uEtGyH\nARCq69kcWKLAUjFpLhn2BTH9QZOqJDx8/RNc/ZmHmdzleMPAcMjrP3oyJ7z2WDDQsCGRj3HOvM9G\nen9we2GIIXCKqj4NICJLgS8DLwRuAboyxKJ3OfsMxVL1qnJNmv4zpSb4JkStCsu//GtP9fwjANId\nuwjqgmlC0HCSYRC7Noe5B7JTOuxEgRm22Q5z5jgvkp6b6LAfZmYJk/h5Tr267J0o5G0hLN09jr5P\nsgkgDCEKsdUQWw1Iq25bHrSb23Xbjcm5lzlNXXP0JM3VcLECRtHAINaiFkSDdkm/vL7PLjqvu3eo\n2ADXW7vipMMgSqlGCX1RzEDUoBYkDIZ1FoZTjIQT3Put9Xz1Yw+RFso2T+xK+LeP38/lCKdefBQN\nE9IwIaG1WCMYq/su7EZEasDRGTP02IIrqDoJHc1I2o5tRZhnXDrT/SNJqUhKBWc7DET4xjVTXPHZ\nzTy1MWHVipB16+fZ5EkMwRSEUxDUnYQYNNUxxIIzJe83M0tKQ6eqLG1SxOGNtowU1YLarF5tdhKa\n66ud/a9dHopWFoNEIVKtoLUKtr9C2h+R9AfYqCUZtgfIq1N/M8dJlgnjz4f1TDk7T4j7HgROQszU\n5kyyLCXDZ46iWmpmmU/xTo8Q1xAsAo0sUSWhUknpq8QMRE0WRA023PAw1//NXezePMXi5VUak0kb\nM8yQxsoNf/kI575uOXUTUTUJTRNiVQjFkojt+RLPJSH+f8DjIvIt4Gt+3eXA48D/BnbNdGAx7SZf\nckNnlrLnGtdcc80kH/jw7txeOG9mCKCWoO6kw6CZSYjqs1QK0oTFB+v6wzrMC9Pshh3M8LCXIjrt\niB3fUSclZs6T2VJDxQgSeMmwEkG1gq1FJH0BSZ9XmaUlkWZhMqjk11GyXsq2de6cMeYnEqeStaoi\nl+aPbjDCtPYjIt1fZjNhpm2q3oGS9bfB975RiFxGW2Y7rAUxG254mB995jaSuiNo+8bGrKTv3NRo\nmeIkbeM188FcDPEc4FzgDcBL/bovAd8A1qjqaXOdIMs/zAjMCA7EZaAEwGeuHO/ZeTITosERKruV\naEq9yuw8nsaHZAAtw3zGBA1068VbZIZtKWIlCoyvIC1mtkOrPodZvffYz32ncV48IwwCpBIhA/3Y\noX7S/ojGogobtt/N+lu+Qzy+k2jBCCvOei1Ljjrb2YUtBKgbsuBkkYwZWus82wWGKN6mqNa6653b\nND1zLaXEfY9OKdF7Wm0k2AjW3/h1xu6+zd0bRlh00Vmc+J5XMRA1GYwafP9v7s6ZYS9YvLxCv2kw\naSpMmgqhcU3qjYSEpnemOBdD7FdVFZEf4RrEKHC7Xzer6yYLtwFyTm188YYAxRQiZ9fPw3nS9VxB\nxKozLqK2yxI0rAsMVrztsMXJ8oblRh1zzMWPjgGLNqVCiljn9sMWneaEggpNYcmlQzGgBRVWDBKG\nEEXIwAB24QCN0T7iwZANO+9m3U+/jibOGhOP7+TJW7+GBrBk9TmYtEWDSQS1hRxndQxYPVPMz50F\nevsgfaAMsn8W0DVxI78HXD65jYQnb/0GY2sKRfetsuO6u7jtOte3Toy0xarOhTCC3/jAcmompmpi\nL3RZrHeuGLvvwm62iMhHcVVubsKxjv8tIp8HtvZyAlNgikFBzArEpeZcc81kT4TOhuPPupyli88i\nmEhd2EeS2Q29pJddJ+NzYymoAU6EnV44AKYxw1JK7Ax5of3lkHnxZ0jXyv8NAscMKxHaVyUZqlFf\nHNEcFDZ8//qcGWbQJGbjndez+NhznHTpbcFZaE6bNFL0YmdEqZk5uqDE/DBf77KIC6UKXF9sG8KO\nNbN3IJmNGQ4Pu89d3lg3PCK8+5OjnH3JQiatE7YCL3w5k9z8LvZcDPFDwI+Av8R5lAGeBj4NXDjb\ngc5+6P4vur2d6uxe09++ZpI//ujueRHciWp1mNW102B7w6VuZYywKCX7wFwNjXteQxffhvptmRez\nSH83Rlg+SEA7I5RuErR/aMRL5KKCWp8tkjlR+vuQvhrxEUOMr64xdqyhOWxJdu/ses54fCe24gVN\nFSRVbOhiOLJ4xfwSzvJAieqMl3EeCQ3PXYhBTHs2Uc4Ep9kSe4jvE0GjAFsJSSuGNHpmk3zZ6/v4\n3GcXEavFAnussstW2J7GWGOIJMlLCgYF30WvmJUhqurtIvIYzqP8Vr/6fuAXqvrTvflBFsGqIVXD\nn/7hLqam9maUFk4cvZBwx0T3jd5uoSLOK5mpTZrFvfnG5V5r7nwgOiWgUs1i7pdCXsPQSW4i4nKI\nsywRzwxZtJBksMYT+gDrvv0dkl3dGWERaUWcbdiCTQWb4Jitj11sy3CZz08qG9T3Nm9d9nGVg4oe\nZtP6HgRoFGIrAVoxLt7wGU71F/95in/68gYAFo0In/rDQV5+aUBFUupqc4dKJCkJQS4p9nraXkK4\nbwcuxfVk/Q/gPP/5jHDTv+9ibOdeRsJ7LKquYkV0HDLl6u1JI0YaWe5sa5E0daWhEtvmAKBQxLQt\n0Fi7SUEFlesZUf0cQpE5GmndTVlIjc88wYhzoAQmd6Ikw/081XiAJ27/ek/MEOChr/wvX8vQhW64\nggBZwVHTUp9nCfvQDvNIiVkwjwyPrk4UY1w5tyx0qrMuwF6gKJPs2Km8/327ue7aiZZ/wi+OpOyz\n95PO4RiRTwLn4xpLfQT4DVwD+4/MOuoMBKTqyvKkCP/yZ5t6JrLLCVhZOYXzBi5CxyfQiSl0cgqm\n6tB0TFHiJF9IXB0+l1LWwRw7mGTboq0YxralhEOmSeU5qZ4xBQYJDATOeSJhiFQqSK2KLFhAumQh\nj8sDPPzzr02zF86GeMfTrL/p66TeU5lGQloRbMWgkYEgcPUVjXGB3jljllxizaDFys6wV5JliQ4Y\nz078C5AwQI3JnVlZ3nu0aOk+O2WawpVXjBH4qlju9DZngrnfYh/FIf468PxCHcTvqOo1c446yys4\nU5m3ber9QSjiNSNvd+lgaYo2mz58w3uNTeAkueztJOIeygSnVqV+vdFWGSgRl71gZi4D1empPmzR\n5WXg5kNbzMXgJYPQvc4D69QqH2+oA32sazzI2nuunh6O0wN23ncbK192eZ7ypYE4aTF00oha6+4J\na8F66dQUmSJdpJm9mIsSXSE+60iyF6O3MRfTO+MdXUsg7DV27fSMENsWvVJ06PaK2eoh7sH1X95c\ncKcP+PWqqkO9nsSq+MUQ24DYBCxeXmH7xmbPhALUZAA7XrAXioHAZyB4xqiaqWrWSQqqELaM6ZKp\ndurTwnxaT55a201mzubYFEJLSjj4vFSVTH31alIY+N4X/t6pVthQf5RHnvgRjWq/pdUAABfhSURB\nVMaM8fxzQ60P6MU7VZSgIs5OFfkXYpy4zywW0T+cucfTM+9Mqi3+lhIOc4a+zKROG0FCH2NqfehV\nXoAjUx6f/ecnKw82Xy/zjCqzqg7iVOWb/HKz//6fwM0i8u+zDawqPi63xQwTNcQaULcRr3vvsfMi\nVDAcxxmucoqvnpKXlSpWYvZlpvLg3GxdavMshlYRUb+u065YyNNt81Z3fj/cIO3/Z6oyUuhREgVo\nJUQrEVqrQK0K/X2st0+w5unrnxkzBBDjGaLkTNFGTm12xvsQjULPlEO3BCYP/WgrFiEdv+Mwx7wK\nQHdDlnWUoS0uNVv3zE7RFeLqIlq/dN9l38QhXtrx/c9o/aR5zZ7FNXyxKsQacNbrVrDh7m18/6vb\n5jw2kionyVks05Wtlaq4GDMv1uVpWbZ1d1tXmlxSJ0W2XSDrQjcQdcn/vnqA+F5/M+JwVpkhZyBF\n5OXgs5JdofMmYy3qPY0PPXgDqs8sAB9g6OwX5cUeNPAJMZnaHAWoVXfuNMv1w6nMnim2xZx2+x0l\n2tFLGTAfTSCZzRYKz1nhWXq2NCs/bFq4oGlP/uLpmIshDgMrVfX/AIjI7cAST8KsjhUXziI+vdVV\nr01sQMO62mWTtsJlnzwN5UF+cNWW9vCWasTqF72Rkzevgs1b0WYT24xb2Q7TztVh/bPOpuieFkNe\nHjnLs/UVeNSSx7QhriIK+Li6mZrjlOoy4FRNEc1r2tlQMAq2YhAbuvmzFkRIByKS5JkH4BOELL/4\ncjQm9zSLVScpVoW06orwZZWz8wIPxjjGHHjJMGuFWaykUzJDh275zD0d551a4BlhobrRM9Cq/vuf\nnMzVn3mEPbtmHmTZioBYXbe9VA22s0DBPDDXkR8GiqpxBZfb/DJc9sqsUBVSa0isIbWGpg2YSiOm\n0gp70hpjaT933bR7mhitjZjNt12HTNbz9C8xhTizfHH2wq6ifmeFFWtb61R9AQDy3Nu86nLuTS7s\n61FmOLSkQbL2nX5x1UvEBd9WA9L+kHRBheZwlYll1X10csvYmp/ldksM2Fx9ztTmlupMJYJK5FVo\nZ0fUsFV8NpdqpfC7SswdbiOtIHvE5HnpANi0ZTfMUAzHmgeGlvdx5utW8vnbz+EHj5/IFX+xmFpf\n+z61Pnjnh4aJNXQ928mYojfVzfOizsUQK6paLEz4I1XdrqrrgIHZDtSC/dApt653aqyGhg2ZSiP2\npDV2bq53PT6zNUnuMZ6+SB535lShOW0gHUyuM/c2XwftITZt22c/xWGBokMid6b4uMBQSKuGpBaQ\n9IXEQyFTowYZ6Jt73LlgLdtuvC5XbXNHjmnFJdrIONU5ChzzC41jhhlDzKpwZ0wVSjPIDMibes3G\nIL2gInkFdGbNFOo5/KUWcOG7T3M+B42oa8QvXTrCJ64c4cgVBhE4coXhE1eO8IpLF5IipOwdE2w7\n74x0i6wCThaRHwLXA3+qqu/y267Fqc4zIlOXUysYcY2jmyZgMqmQmBSrwniaMrB0gInN0zNNapWF\n7kaNwtbPM9I+2SazW2ThNF5qzC/kDBPTyQgtLhRHnWctN4Nk6X1SOO5wf3g8M8w88pnHNyvHpQJi\nJW/+Xh8WJlYpR/3uL7P2T2f1w/WEZMwFcWcMTSx5CI71aWG24ggzWekqkdyh4pik5Omac9bHPJxg\nTHtRjKLtsIMp5tKhfx7Ue5GdTT7bSXKGWXReRYMjxHumB+OLEVSVgaUDnP+uMznxtStp2CnqNmK3\nrZEiXPD6ES66dEHeh7mJYY+NqNtKS0rMJURDmnVc7PECz2ZD/EfgAZxXeTnOs3yJqm4HzgZ+PNvA\nuQ2xoDbHNqBpwxYXT+GE3zqfe//kP7GNVg1EE0Qcv+KVgA+wjXBqLbQM5RmyYFzpkRm2CMyZoRhF\nrYB4p4q3QYrqdKZ4uCN3oLivLZVZ3DOT+bNCIa1Ac1iIhxNOPO9Y1v9VQDL1zBwr4cKRNjqK9Fjf\na9kGrh1Bm6DibYdF6TYLuWlJnOVF7gVt7WB7Pqjl1V/6kovZ8IN/cx0ZPUw15MwPv5wTXnsMfUHM\nQNAk1jSXEGMNqVulLnFeLcuqoUngGaG3I+J4i/M6+xfkvui6h5MAXw1cCzRwxWAf9rnNi4D3znSg\ns6kK1gqJGFSVwBim4qjV5yBWEjWYF53Hst8Z5Okv30i6YxeVgWGOPukiloenoLsmkErkmpgDpMaF\n2bTNs7TsF9nbCFpvtC4XL29UXiC4zV8ikjtmSqbYDnV+p9ycYEPnnc+ktTRyqmtaE9IqNBYp0XCd\nkdrkrPXtlq4I2LIxJaoJzRlqY0oUMfqKi9vpKTBDGzoBxVaME1QMbTGkNjCtKjlBi5l385wfrpDM\n0ZQVNumIR2xTozNzFTgbffYcuoFan5ln37+IFp52Ds2FyvYbriPdtYtodIgTf+t8lr/qBKw2SdU1\nijJqib0jdsJWSUUIxPpey+7CxhoyYatOrbYRDRvlEmLqpcT5YDaGGAG7VfV8EXkFcBquUvZlwJ6O\ntgLToFZIU6frqyjEIWlgSaxx3kkVGnHIxGSV6KzzWL3qfPo3GQY2Wip7LHZ7AxN5w3hifPEF30Gt\nQ22mUzosoE2lziSCwn4uYDsTa4CgZSfMJMUSLbQV2PU3uRUh6x6r4srCJ/2Q1pRkKGW4r8lA0GRo\nWR+7N02v5rFsRcA/3XIik1rlqeZivvDJJ1lz9S/arnM4PMLoKy5m4ennQNJ+/owp5pJqJIjNRNjW\nvjaUlvqcxyG2HtbD/oVXlJCNuAgNtdMlwrmcLh1aWh6r6u29amDBOefQd+FZBIsaDA9NMlSrYzXJ\nVV0XtxzkccuTtoIVIRAlFVfVBhxDzJhhUVLMxsj8GL3GP87GEL+AayR1s6reCNzofqP8A/Ancw2s\n6iTENDUYY0lFIDWk1k1SYg3NZkjSCNF6gGkKkhQ0YtNKxxI3oGOKqfWZJlmlT9OyJXZDxgxnJ7a1\nb7chMimxuO9hiFa8oV9RsCfmLxEDacUxw7QKVF3jICPKy959Gt/59F006y0pv9YnvP2Di5nUKhO2\nyqSt8IIPv4RF77yUTTuHaE5FMBYRThiCunQPCSnQpZpJgM7GlJcm6wjGbg8sf/bm7JBBNgdFO2LX\n/VqaV0+B3JkdsSCNd758svdeK4lDcgkv1sC3Fg0IUOo2AtOKOXSqdIsZ5vZD72CZL2ZrVP/nM6y/\nW0S+PfuwjhkaDCkWawOsNST+qVEV0lRImiE6FWDq7mY33qSQeSs1CpAkbNW6E0HCzPZXCLrNTyvd\nGVYxub8X22J2TIlpyFVm/yGeCeVCdgC2qqR9iq0pYTWlErgH7JSLV7M42sN//PnjbN8Us2R5yNs+\nNMq5lxzBrrTGpK0ylvQxkVSoJyFpEqCpwaTSqpSi7Z7+3P6XqcKqrbhIPG0qBabp1WUvtbQKVJTX\nO4OItFc778RsWlPGKPMg+MJSkBDzlyvkfoZQbCGjzZD1Va7biEhSrKclxRAQEYil6RlhXUNiG9Kw\nXkq0jpFmtRN6FWN6aUPaDe8H/mLGrepUZud0cjqoTclVZVXBJoI2HDM0DccMxWpLDQsMJgrQVNsL\nNaS2LQymK4o9M7IbvVscYy8o1eY2aBbnXhSYCxKXjZRtN3+XeNdWFr/z1wmjlChI8wyl0//LKi58\n/QgDpunsQeq8hGPpAJO2wu3XbOSeq29l9Wd+kzQxkLSYoSis/+rfMXTyWYyceh4777+dnff9lOMv\nezeIcte/fpTTXvcB+muLXc1EaCs5ldXj63wwi2E8hy+kTTrstCXOiSzKIx8uq2ZOPue5FN/BY9VL\nhIlnhmFW9wAI1Tlk6xKRisGIJbVCJKlzoKmhriENG+XqcmKDgg1xfmE4e8sQ5z6D9dmD4sMessM8\ns9TsZvc3fCvxu3DD+kBapzI7I5V6KVA6L1SxdHw+4e0XqJT6ZoeIrAWW4hTTCVy41btUddztQJYl\n6VCwzzl1SF03NeOcLARKEDhJPlN7GjaibipgXWmmVIW6Vpi0bmn6mzlOA7DiljxYHla96beRlK6q\n81lv/qzbFiuP3nkV1dowR536mhZDNJkNmem2w/LWcMiku1wDk+k2e5jdRNXpUOnGDPOHXaapyrnd\nD2cHjDUg9CayhleZM2dJihDbLuoymS1yfhd2bxni7K8NBU39jdwxcWrFvboTg8QGk4CJJbcd5sbx\nyGATBevjyXBjib9Qube5KCn61LxpfTUKx2uHpDhvVem5z1QvUdXvi8gK4LvAx4HfzzZaL3JJwVSR\n3+gG11YyAEQxocX4jmdZyNWkrRClKbEJCLDeKB4ylvQxnlappy4SIY4DNBFfR0/aW53CtO+5pJdL\nfq2A7cKz1+aEmVbx5jl/aWeH5IILLpTN+jayHRpSzgzz+of+s1gUuEMba3dktcbK6j6kKl4ybBWC\nCbFeagxIbIr1503VOAmR1os2tkEuIcbWq8xtzLU3zFX+qxvjE2DutAP/ZtfsiMyWk+UPx15NTltq\nTTGEIqueIqnBGnHzGogLwVF1zaIs7eW4UptLkJ1SZO5Zzi5WW4jOLNLjYaouq+oGEbkeOF1EbgJ+\nLFGFX3z691n1ng9hoogt136d+pNPYPr7GX7ZKxh88Ytc43GjkMZs/auvsu6eR3hy5TAv/sQFVE8e\nZDKt8JN/eIR7vrGW8R1NFi7r44J3ncbSC0eZSCs00hBrhaf//jr23PJzgoWDLHn9GxhceRKi8ORX\n/g/Dp57DotNfNI3mu/7xA5z2qx9lfP2jbH/yLhBh06M/ZOGS4xhccizj25/kxJf89zxdb+2d14LA\n6hdcdpirywVk0mGWmVW0J9JFMpQuEmPh+coD4gsSYpsjS8Fak9c7SAtqc4L7jG2AISI0KVgX1RBL\nK8THqcmGFJcFl3jJMCl4mnvFbE6VwZ5HmXYw3oAjXdZL/im2i7rsXZYa4rIKsnJcmaydFWqwIGJ9\n0Rt/UcDlKANZrcOuTpIik+shTOlwNLj7TKWLgauBC4A3hyMjrPq9DwCw8Qufp7JsGUf9P5+iuW0L\nm/7+bwmWLqJ26vEATN71IEe8+9c4+kOvZ/K7P+LHv38Dl/zbG2mEIYMrBnnzFy9gwWiNNd/dxL9/\n7E5+5RtHIyMDxDZg4uENjJxzJis/9ymm7rifp7/8Jfrf8weYyGeLdkiHjmC/SWDJSS9mYutaKrWF\nrDrztYhCc3KM9fffQJzUCat9qKbsWHs3J77ytw57yRDw5hBx5e+gTXXuqh4XNISWxCjttvpilkqe\nMy7T5jtL83UBAe1SXWID11LUp/4GxhITtNU5TKxpC7VJC17m+XqbRWdzTuwlRGQr8OQ+H/jgwVGq\nOmvq4qEIb0McxUX6jQHfBj6AsyXeAvwO7rpGwJnA3bRCn1f49WuBI4Eh4KHC8Gfi4ljHu5z6VGAj\nLvh/sR/r3sL2U3DdHncAJwHbgW1+31HgYb/fOcAaXCLB0bhe4hsL45wA7PTHLgRW4pqmFfGcvLZz\noXxmPdTXESyXcsExs1d1WX8T8FuF7y8Etnbs807ge/7/K4CvdWy/A/h1//9bgHtwDHAXjgG/3W97\nK3BHx7FfAz5SoOV/FPb9UWE/BY73/38R+KOOcd6Ei6sFuAr46IGe83I5uJb55bWUOJxRVCU2AotE\npGhWWQ1sKHxflf0jIgYnjW0UkaOAvwfeBSxW1WGcVFfUa1ZIu562mnZJb770ZrgWOFNETgdeB/zL\nPMcs8RxHyRBLzBvqSsL9BPisiNRE5Ezg7cBXCrudIyJvEJEQl/feAG7DlY1TYCuAiLwNOL3jFEcA\nvycikYi8EacyXzdPMp8Gju2guw58HfhX4HZ1ZexKlMhRMsQSe4v/irPTbQSuAT6lqt8vbP8mrmvj\nTuDNwBtUNVbVB4DPAbfimNYZTK+c9FOcvW8b8MfA5eqqLM0H/wCcKiK7fLm6DF/y5/zneY5X4jDA\ns+JUKVHiYIWIrMY5e5ap6u4DTU+JgwulhFjisIG3Zb4fuKpkhiW6YW8zVUqUOKQgIgM4Ff1J4KID\nTE6JgxSlylyiRIkSHqXKXKJEiRIe81KZR2WZNmm6L1mNQqDLP9PT9qRze7djOg6Y9fsM43Z8zath\nz5S9M8c5dNo+Mx2brZP2ALgZae44R4/nmXPsWdbP+7cU1k/TI2bLhuqybcbsqXmO4+qAzXOstvXa\ntr7rIfktM117mp7FVmhfNG28LB+4c/i89lPH7V9Yn28v5BF37Nc+ts66vW1d5/7TzjHDWNm2jjE7\n9239pOnn6LqtowVU2++fNr/Stk/7/sVtrb8/u7fxXVXtyUwyL4bYpMkLzS+3Om516XInefGEVsl+\nVzTSdOQ5mvyYrgVcO/fPtvtjVMTXWiseM319nodsCtuF/Jj2UlA+77JwTN5qs3iM+Jk20lZBpUUD\nMx7T1sOjc9u0sYrrZNq2uY5p+58CQ+xc323/jm0zre88vvsxOiPdrbtZu/wGbTsmG6v9PO67dGxz\nn9m21nrp2F+k8IBLa3/JvvtPd/tl+7eOydYZWvtkx+Tb8u2aH9O2MPN6kx9j29YZXx4qyNe57QGt\nbUbUb8+2FddbDP5TLAGt82RjBP44gADbfgyWoLAt2z87n6PNj+GPyZpDtbZpTldQmIOA7Hfgj8Ef\nAwGu8G8ggi8M6NeJX2f8/+4ogyFY/ugoPaJUmUuUKFHCo2SIJUqUKOFRMsQSJUqU8CgZYokSJUp4\nlAyxRIkSJTxKhliiRIkSHiVDLFGiRAmPkiGWKFGihEfJEEuUKFHCo2SIJUqUKOFRMsQSJUqU8CgZ\nYokSJUp4lAyxRIkSJTzmVSBWRL6Dawx+sGAU14joUEBJ67ODQ4XWQ4VOeO7Ruq3X8l+HdMVsEblT\nVc890HT0gpLWZweHCq2HCp1weNNaqswlSpQo4VEyxBIlSpTwONQZ4t8daALmgZLWZweHCq2HCp1w\nGNN6SNsQS5QoUWJf4lCXEEuUKFFin+GQYIgicpGIPCwij4nI73fZfrKI3CoiDRH54IGgsUDLXLT+\nNxG5V0TuE5GfiMjzDgSdnpa5aL3U03qPiNwpIi89GOks7HeeiCQicvn+pK+Dhrnm9GUiMubn9B4R\n+eSBoNPTMue8enrvEZH7ReTm/U2jp2GuOf1QYT7XiEgqIov26mSqelAvQAD8AjgWqAA/B07t2OcI\n4Dzgj4EPHuS0ng+M+P9fC/z0IKZ1AS2zypnAQwcjnYX9bgSuAy4/iOf0ZcC3DgR9e0HrMPAAsNp/\nP+JgpLNj/0uAG/f2fIeChPgC4DFVfVxVm8BVwKXFHVR1i6reAcQHgsACeqH1J6q603+9DVi5n2nM\n0Aut4+rvMmCALu2Z9wPmpNPj3cA3gC37k7gO9ErrwYBeaP0N4GpVXQfuOdvPNML85/S/Al/d25Md\nCgxxBfBU4ft6v+5gxHxpfTtw/bNK0czoiVYRuUxEHgK+DfzmfqKtiDnpFJEVwGXA3+xHurqh1+t/\nvjdFXC8ip+0f0qahF1pPBEZE5CYR+ZmIvGW/UddCz8+UiPQDF+FejHuFeTWqL7HvICIvxzHEA2KX\n6xWqeg1wjYhcCPxP4FUHmKRu+AvgI6pqJesgf/DiLpwKOi4iFwPXAiccYJpmQgicA7wS6ANuFZHb\nVPWRA0vWjLgE+LGq7tjbAQ4FhrgBWFX4vtKvOxjRE60icibwBeC1qrp9P9HWiXnNq6reIiLHisio\nqu7PPNde6DwXuMozw1HgYhFJVPXa/UNijjlpVdXdhf+vE5G/PgBzCr3N63pgu6pOABMicgvwPGB/\nMsT53Kdv4hmoy8Ah4VQJgceBY2gZVU+bYd8rOLBOlTlpBVYDjwHnH+zzChxPy6lytr8R5WCjs2P/\nL3LgnCq9zOmywpy+AFi3v+d0HrSeAvzA79sPrAFOP9jo9PstBHYAA8/kfAe9hKiqiYi8C/guzuP0\nj6p6v4i802//vIgsA+4EhgArIu/FeaJ2zzjwAaIV+CSwGPhrL9EkegAS6Xuk9VeBt4hIDEwBv67+\n7jvI6Dwo0COtlwO/IyIJbk7ftL/ntFdaVfVBX+HqXsACX1DVNQcbnX7Xy4Ab1Emze40yU6VEiRIl\nPA4FL3OJEiVK7BeUDLFEiRIlPEqGWKJEiRIeJUMsUaJECY+SIZYoUaKER8kQS5QoUcKjZIglSpQo\n4VEyxBIlSpTw+L86ZBC5880JJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd3e2a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
    "    classifier.fit(X,Y)\n",
    "    y_pred = classifier.predict(X);\n",
    "\n",
    "    #y_pred.ravel() == Y.ravel() 根据预测值和实际值对比，生成一个[Frue, False ...]\n",
    "    #然后求数据的平均值\n",
    "    classif_rate = np.mean(y_pred.ravel() == Y.ravel()) \n",
    "    print(\"classif_rate for %s : %f\" % (name, classif_rate))\n",
    "    \n",
    "    #使用p're\n",
    "    probas = classifier.predict_proba(Xfull)\n",
    "    n_classes = np.unique(y_pred).size #获取分类的格式，次数是3中 (1,2,3)\n",
    "    \n",
    "    for k in range(n_classes):\n",
    "        plt.subplot(n_classifiers, n_classes, index * n_classes + k +1)\n",
    "        plt.title(\"Class %d\" % k)\n",
    "        if k==0:\n",
    "            plt.ylabel(name)\n",
    "        imshow_handle = plt.imshow(probas[:, k].reshape((100, 100)),\n",
    "                                   extent=(3, 9, 1, 5), origin='lower')\n",
    "        plt.xticks(())\n",
    "        plt.yticks(())\n",
    "        idx = (y_pred == k)\n",
    "        if idx.any():\n",
    "            plt.scatter(X[idx, 0], X[idx, 1], marker='o', c='k')\n",
    "\n",
    "ax = plt.axes([0.15, 0.04, 0.7, 0.05])\n",
    "plt.title(\"Probability\")\n",
    "plt.colorbar(imshow_handle, cax=ax, orientation='horizontal')\n",
    "\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 问题记录：\n",
    " > 1.模型的predict_proba 和 predict 函数的区别\n",
    " \n",
    " > 2.上述的图用来说明什么问题\n",
    " \n",
    " > 3.'L2 logistic (Multinomial)': LogisticRegression(C=C, solver='lbfgs', multi_class='multinomial'),和平常的有什么区别\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
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